Research

I'm working at Google on... *whistles* ♪ ♫

Past Projects

My previous research project with David Aha at the Naval Research Lab was on how AI can be used to overcome information overload in multiparticipant chat (e.g., IRC). The NY Times recently wrote an article about this from an Air Force / Army viewpoint. While this research project was in context of problems seen within the US Navy, we took a general-AI approach to this to make it applicable to other domains.

My PhD research mostly focused on the traveling tournament problem (TTP), a brutally-hard, combinatorial optimization problem from the sports scheduling literature (I call it the traveling salesman problem on steroids). I approached this problem using two types of techniques: metaheuristics (ant colony optimization) and heuristic search (DFS* and IDA*). While the ACO approach did average, the heuristic search approaches did surprisingly well, which can be seen on the TTP website with all the optimal solutions we (Uthus, Riddle, and Guesgen) found. The DFS*-based approach (presented at CPAIOR 2009) solved CIRC8, SUPER4-10, GALAXY4-8 and found new lower bounds for NL10-12, CIRC10, SUPER12-14, and GALAXY10. The IDA*-based approach (published in Journal of Scheduling) later solved NL10, CIRC10, and GALAXY10. All optimal solutions are available under the Files section.

Files

The Ubuntu Chat Corpus: Here
Optimal solutions for NL, CIRC, SUPER, and GALAXY, sizes 4 to 10 teams: TTPOptimalSolutions.txt
Galaxy instances for the TTP (also available on Michael Trick's TTP website along with our best results): GalaxySet.zip
Super 14 Rugby League instances for the TTP (also available on the TTP website along with our best results): Super14.zip
Problem instance files used for the single round robin maximum value problem: srrmvp.zip